Overview

Brought to you by YData

Dataset statistics

Number of variables 13
Number of observations 3225859
Missing cells 0
Missing cells (%) 0.0%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 319.9 MiB
Average record size in memory 104.0 B

Variable types

Numeric 10
Text 2
Categorical 1

Alerts

gam_score_se is highly overall correlated with gam_score and 2 other fields High correlation
gam_score_90mse is highly overall correlated with gam_score and 2 other fields High correlation
gam_score_90pse is highly overall correlated with gam_score and 2 other fields High correlation
gam_score is highly overall correlated with gam_score_se and 2 other fields High correlation
nichd is highly overall correlated with norm High correlation
norm is highly overall correlated with nichd High correlation
gam_score is highly skewed (γ1 = -68.000823) Skewed
gam_score_se is highly skewed (γ1 = 635.0719086) Skewed
gam_score_90mse is highly skewed (γ1 = -634.9143922) Skewed
gam_score_90pse is highly skewed (γ1 = 635.2238839) Skewed
DE is highly skewed (γ1 = 168.5327063) Skewed
nichd is uniformly distributed Uniform
norm has 460837 (14.3%) zeros Zeros
D has 126892 (3.9%) zeros Zeros
E has 538729 (16.7%) zeros Zeros
DE has 2453256 (76.0%) zeros Zeros

Reproduction

Analysis started 2025-04-28 13:23:11.110191
Analysis finished 2025-04-28 13:34:19.469000
Duration 11 minutes and 8.36 seconds
Software version ydata-profiling vv4.16.1
Download configuration config.json

Variables

atc_concept_id
Real number (ℝ)

Distinct 1088
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 22091329
Minimum 1588648
Maximum 45893497
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 24.6 MiB
2025-04-28T20:34:19.597321 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum 1588648
5-th percentile 21600399
Q1 21601751
median 21603179
Q3 21604256
95-th percentile 21604857
Maximum 45893497
Range 44304849
Interquartile range (IQR) 2505

Descriptive statistics

Standard deviation 3297360
Coefficient of variation (CV) 0.14926037
Kurtosis 35.926151
Mean 22091329
Median Absolute Deviation (MAD) 1165
Skewness 5.6364971
Sum 7.1263514 × 1013
Variance 1.0872583 × 1013
Monotonicity Increasing
2025-04-28T20:34:19.791431 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
21603929 26880
 
0.8%
21601423 26425
 
0.8%
21602256 24227
 
0.8%
21602735 23891
 
0.7%
21604344 23128
 
0.7%
21603967 21000
 
0.7%
21602734 20664
 
0.6%
21602732 20356
 
0.6%
21603911 19705
 
0.6%
21601390 18879
 
0.6%
Other values (1078) 3000704
93.0%
Value Count Frequency (%)
1588648 42
 
< 0.1%
1588697 3339
0.1%
21600005 1414
< 0.1%
21600008 1309
 
< 0.1%
21600012 679
 
< 0.1%
21600013 2142
0.1%
21600019 1239
 
< 0.1%
21600034 2443
0.1%
21600056 518
 
< 0.1%
21600082 1778
0.1%
Value Count Frequency (%)
45893497 665
 
< 0.1%
45893489 168
 
< 0.1%
45893488 4284
0.1%
45893476 14
 
< 0.1%
45893474 1323
 
< 0.1%
45893464 616
 
< 0.1%
45893463 413
 
< 0.1%
45893461 350
 
< 0.1%
45893458 84
 
< 0.1%
45893267 7
 
< 0.1%

meddra_concept_id
Real number (ℝ)

Distinct 10770
Distinct (%) 0.3%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 36482484
Minimum 788090
Maximum 46277190
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 24.6 MiB
2025-04-28T20:34:19.983132 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum 788090
5-th percentile 35204955
Q1 35808819
median 36311982
Q3 36818654
95-th percentile 42889379
Maximum 46277190
Range 45489100
Interquartile range (IQR) 1009835

Descriptive statistics

Standard deviation 3066521.4
Coefficient of variation (CV) 0.084054623
Kurtosis 85.529364
Mean 36482484
Median Absolute Deviation (MAD) 503188
Skewness -6.6525331
Sum 1.1768735 × 1014
Variance 9.4035538 × 1012
Monotonicity Not monotonic
2025-04-28T20:34:20.173975 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
35708208 5705
 
0.2%
37522220 5593
 
0.2%
35809327 5523
 
0.2%
35708202 5404
 
0.2%
35809054 5376
 
0.2%
35708093 5152
 
0.2%
36718132 5131
 
0.2%
35205038 4921
 
0.2%
35708154 4900
 
0.2%
35809243 4837
 
0.1%
Other values (10760) 3173317
98.4%
Value Count Frequency (%)
788090 7
 
< 0.1%
788094 175
< 0.1%
788095 21
 
< 0.1%
788096 21
 
< 0.1%
788098 63
 
< 0.1%
788100 56
 
< 0.1%
788104 56
 
< 0.1%
788105 77
 
< 0.1%
788115 259
< 0.1%
788120 154
< 0.1%
Value Count Frequency (%)
46277190 21
 
< 0.1%
46277169 35
 
< 0.1%
46277163 21
 
< 0.1%
46276846 28
 
< 0.1%
46276844 126
< 0.1%
46276840 21
 
< 0.1%
46276826 14
 
< 0.1%
46276825 63
< 0.1%
46276824 14
 
< 0.1%
46276815 35
 
< 0.1%

ade
Text

Distinct 460837
Distinct (%) 14.3%
Missing 0
Missing (%) 0.0%
Memory size 24.6 MiB
2025-04-28T20:34:21.047297 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Length

Max length 17
Median length 17
Mean length 16.98953
Min length 14

Characters and Unicode

Total characters 54805828
Distinct characters 11
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row 1588648_35809076
2nd row 1588648_35809076
3rd row 1588648_35809076
4th row 1588648_35809076
5th row 1588648_35809076
Value Count Frequency (%)
1588648_37522220 7
 
< 0.1%
45893497_37622518 7
 
< 0.1%
1588648_35809076 7
 
< 0.1%
1588648_36315755 7
 
< 0.1%
1588648_36416514 7
 
< 0.1%
45893497_36718379 7
 
< 0.1%
45893497_36718401 7
 
< 0.1%
45893497_36718404 7
 
< 0.1%
45893497_36718422 7
 
< 0.1%
45893497_36918869 7
 
< 0.1%
Other values (460827) 3225789
> 99.9%
2025-04-28T20:34:21.703711 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
1 7765989
14.2%
0 7328559
13.4%
6 6913893
12.6%
3 6699959
12.2%
2 6436150
11.7%
5 3718862
6.8%
4 3479973
6.3%
_ 3225859
5.9%
7 3155026
5.8%
9 3082002
 
5.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 54805828
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
1 7765989
14.2%
0 7328559
13.4%
6 6913893
12.6%
3 6699959
12.2%
2 6436150
11.7%
5 3718862
6.8%
4 3479973
6.3%
_ 3225859
5.9%
7 3155026
5.8%
9 3082002
 
5.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 54805828
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
1 7765989
14.2%
0 7328559
13.4%
6 6913893
12.6%
3 6699959
12.2%
2 6436150
11.7%
5 3718862
6.8%
4 3479973
6.3%
_ 3225859
5.9%
7 3155026
5.8%
9 3082002
 
5.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 54805828
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
1 7765989
14.2%
0 7328559
13.4%
6 6913893
12.6%
3 6699959
12.2%
2 6436150
11.7%
5 3718862
6.8%
4 3479973
6.3%
_ 3225859
5.9%
7 3155026
5.8%
9 3082002
 
5.6%

nichd
Categorical

High correlation  Uniform 

Distinct 7
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 24.6 MiB
term_neonatal
460837 
infancy
460837 
toddler
460837 
early_childhood
460837 
middle_childhood
460837 
Other values (2)
921674 

Length

Max length 17
Median length 16
Mean length 13
Min length 7

Characters and Unicode

Total characters 41936167
Distinct characters 16
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row term_neonatal
2nd row infancy
3rd row toddler
4th row early_childhood
5th row middle_childhood

Common Values

Value Count Frequency (%)
term_neonatal 460837
14.3%
infancy 460837
14.3%
toddler 460837
14.3%
early_childhood 460837
14.3%
middle_childhood 460837
14.3%
early_adolescence 460837
14.3%
late_adolescence 460837
14.3%

Length

2025-04-28T20:34:21.887260 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-28T20:34:22.262991 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Value Count Frequency (%)
term_neonatal 460837
14.3%
infancy 460837
14.3%
toddler 460837
14.3%
early_childhood 460837
14.3%
middle_childhood 460837
14.3%
early_adolescence 460837
14.3%
late_adolescence 460837
14.3%

Most occurring characters

Value Count Frequency (%)
e 5990881
14.3%
l 4608370
11.0%
d 4608370
11.0%
a 3686696
8.8%
o 3686696
8.8%
c 3225859
7.7%
n 2765022
 
6.6%
_ 2304185
 
5.5%
h 1843348
 
4.4%
r 1843348
 
4.4%
Other values (6) 7373392
17.6%

Most occurring categories

Value Count Frequency (%)
(unknown) 41936167
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
e 5990881
14.3%
l 4608370
11.0%
d 4608370
11.0%
a 3686696
8.8%
o 3686696
8.8%
c 3225859
7.7%
n 2765022
 
6.6%
_ 2304185
 
5.5%
h 1843348
 
4.4%
r 1843348
 
4.4%
Other values (6) 7373392
17.6%

Most occurring scripts

Value Count Frequency (%)
(unknown) 41936167
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
e 5990881
14.3%
l 4608370
11.0%
d 4608370
11.0%
a 3686696
8.8%
o 3686696
8.8%
c 3225859
7.7%
n 2765022
 
6.6%
_ 2304185
 
5.5%
h 1843348
 
4.4%
r 1843348
 
4.4%
Other values (6) 7373392
17.6%

Most occurring blocks

Value Count Frequency (%)
(unknown) 41936167
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
e 5990881
14.3%
l 4608370
11.0%
d 4608370
11.0%
a 3686696
8.8%
o 3686696
8.8%
c 3225859
7.7%
n 2765022
 
6.6%
_ 2304185
 
5.5%
h 1843348
 
4.4%
r 1843348
 
4.4%
Other values (6) 7373392
17.6%

gam_score
Real number (ℝ)

High correlation  Skewed 

Distinct 3201135
Distinct (%) 99.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.72957643
Minimum -1583.2964
Maximum 1100.9942
Zeros 0
Zeros (%) 0.0%
Negative 976552
Negative (%) 30.3%
Memory size 24.6 MiB
2025-04-28T20:34:22.523294 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum -1583.2964
5-th percentile -0.47784606
Q1 -2.2655288 × 10-6
median 7.9814651 × 10-5
Q3 0.93100654
95-th percentile 4.0398268
Maximum 1100.9942
Range 2684.2906
Interquartile range (IQR) 0.93100881

Descriptive statistics

Standard deviation 2.8840411
Coefficient of variation (CV) 3.9530349
Kurtosis 56160.376
Mean 0.72957643
Median Absolute Deviation (MAD) 0.1211918
Skewness -68.000823
Sum 2353510.7
Variance 8.317693
Monotonicity Not monotonic
2025-04-28T20:34:22.713943 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-2.479471767 6
 
< 0.1%
65.93800615 6
 
< 0.1%
54.05488581 6
 
< 0.1%
42.34407438 6
 
< 0.1%
30.87659325 6
 
< 0.1%
19.63303383 6
 
< 0.1%
8.542334779 6
 
< 0.1%
0.2151436175 5
 
< 0.1%
1.654897268 5
 
< 0.1%
3.151476826 5
 
< 0.1%
Other values (3201125) 3225802
> 99.9%
Value Count Frequency (%)
-1583.296417 1
< 0.1%
-1126.330337 1
< 0.1%
-861.4814983 1
< 0.1%
-817.4685841 1
< 0.1%
-804.1519415 1
< 0.1%
-703.3395573 1
< 0.1%
-612.1097067 1
< 0.1%
-580.7899328 1
< 0.1%
-571.3190674 1
< 0.1%
-405.5090676 1
< 0.1%
Value Count Frequency (%)
1100.994166 1
< 0.1%
783.8090356 1
< 0.1%
726.8397677 1
< 0.1%
587.9975242 1
< 0.1%
517.7999509 1
< 0.1%
490.2477456 1
< 0.1%
324.3546238 1
< 0.1%
324.1973924 1
< 0.1%
312.4392221 1
< 0.1%
307.908627 1
< 0.1%

norm
Real number (ℝ)

High correlation  Zeros 

Distinct 2285419
Distinct (%) 70.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.50720471
Minimum 0
Maximum 1
Zeros 460837
Zeros (%) 14.3%
Negative 0
Negative (%) 0.0%
Memory size 24.6 MiB
2025-04-28T20:34:22.940912 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0.17607395
median 0.50328406
Q3 0.8293085
95-th percentile 1
Maximum 1
Range 1
Interquartile range (IQR) 0.65323455

Descriptive statistics

Standard deviation 0.33819667
Coefficient of variation (CV) 0.66678535
Kurtosis -1.287696
Mean 0.50720471
Median Absolute Deviation (MAD) 0.32664082
Skewness -0.03209883
Sum 1636170.9
Variance 0.11437699
Monotonicity Not monotonic
2025-04-28T20:34:23.140680 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
1 460837
 
14.3%
0 460837
 
14.3%
0.1656521642 31
 
< 0.1%
0.1656521642 23
 
< 0.1%
0.1610982506 21
 
< 0.1%
0.1610982506 18
 
< 0.1%
0.1656521642 17
 
< 0.1%
0.1656521642 16
 
< 0.1%
0.3317725603 13
 
< 0.1%
0.1656521642 13
 
< 0.1%
Other values (2285409) 2304033
71.4%
Value Count Frequency (%)
0 460837
14.3%
4.617381605 × 10-6 1
 
< 0.1%
1.271763111 × 10-5 1
 
< 0.1%
3.97711657 × 10-5 1
 
< 0.1%
5.064489504 × 10-5 1
 
< 0.1%
5.287215323 × 10-5 1
 
< 0.1%
6.223397524 × 10-5 1
 
< 0.1%
6.562148706 × 10-5 1
 
< 0.1%
6.701805892 × 10-5 1
 
< 0.1%
7.308616937 × 10-5 1
 
< 0.1%
Value Count Frequency (%)
1 460837
14.3%
0.9999999367 1
 
< 0.1%
0.9999994625 1
 
< 0.1%
0.9999979339 1
 
< 0.1%
0.9999960046 1
 
< 0.1%
0.9999957444 1
 
< 0.1%
0.9999956462 1
 
< 0.1%
0.9999906414 1
 
< 0.1%
0.9999903979 1
 
< 0.1%
0.9999881375 1
 
< 0.1%

gam_score_se
Real number (ℝ)

High correlation  Skewed 

Distinct 3201135
Distinct (%) 99.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 3.1151531
Minimum 2.8668088 × 10-5
Maximum 955748.96
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 24.6 MiB
2025-04-28T20:34:23.370949 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum 2.8668088 × 10-5
5-th percentile 0.0013082665
Q1 0.0045930821
median 0.31811281
Q3 0.90509856
95-th percentile 2.4022826
Maximum 955748.96
Range 955748.96
Interquartile range (IQR) 0.90050548

Descriptive statistics

Standard deviation 851.67572
Coefficient of variation (CV) 273.39771
Kurtosis 567079.58
Mean 3.1151531
Median Absolute Deviation (MAD) 0.31555365
Skewness 635.07191
Sum 10049045
Variance 725351.53
Monotonicity Not monotonic
2025-04-28T20:34:23.564975 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
25.49875765 6
 
< 0.1%
32.22233532 6
 
< 0.1%
27.308445 6
 
< 0.1%
23.77487554 6
 
< 0.1%
21.20572913 6
 
< 0.1%
19.90467543 6
 
< 0.1%
21.01185789 6
 
< 0.1%
5.798424708 5
 
< 0.1%
4.297302722 5
 
< 0.1%
3.351460234 5
 
< 0.1%
Other values (3201125) 3225802
> 99.9%
Value Count Frequency (%)
2.866808817 × 10-5 1
< 0.1%
2.903729106 × 10-5 1
< 0.1%
2.999385412 × 10-5 1
< 0.1%
3.23813602 × 10-5 1
< 0.1%
3.265033259 × 10-5 1
< 0.1%
3.279838455 × 10-5 1
< 0.1%
3.348108544 × 10-5 1
< 0.1%
3.387884774 × 10-5 1
< 0.1%
3.511656996 × 10-5 1
< 0.1%
3.533356434 × 10-5 1
< 0.1%
Value Count Frequency (%)
955748.9571 1
< 0.1%
431620.9721 1
< 0.1%
427811.0042 1
< 0.1%
373427.1342 1
< 0.1%
301929.7694 1
< 0.1%
274107.4666 1
< 0.1%
257861.8838 1
< 0.1%
249115.2338 1
< 0.1%
248583.5542 1
< 0.1%
221625.9706 1
< 0.1%

gam_score_90mse
Real number (ℝ)

High correlation  Skewed 

Distinct 3201135
Distinct (%) 99.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -4.3948504
Minimum -1572410
Maximum 47.232281
Zeros 0
Zeros (%) 0.0%
Negative 2658174
Negative (%) 82.4%
Memory size 24.6 MiB
2025-04-28T20:34:23.759093 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum -1572410
5-th percentile -2.6165057
Q1 -0.52355155
median -0.012332565
Q3 -0.0026387694
95-th percentile 1.5253107
Maximum 47.232281
Range 1572457.2
Interquartile range (IQR) 0.52091278

Descriptive statistics

Standard deviation 1401.2542
Coefficient of variation (CV) -318.84002
Kurtosis 566898.09
Mean -4.3948504
Median Absolute Deviation (MAD) 0.20930088
Skewness -634.91439
Sum -14177168
Variance 1963513.3
Monotonicity Not monotonic
2025-04-28T20:34:23.958322 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
-44.42492811 6
 
< 0.1%
12.93226455 6
 
< 0.1%
9.132493784 6
 
< 0.1%
3.234404111 6
 
< 0.1%
-4.006831171 6
 
< 0.1%
-13.11015725 6
 
< 0.1%
-26.02217145 6
 
< 0.1%
-9.323265028 5
 
< 0.1%
-5.41416571 5
 
< 0.1%
-2.361675259 5
 
< 0.1%
Other values (3201125) 3225802
> 99.9%
Value Count Frequency (%)
-1572410.002 1
< 0.1%
-709704.0598 1
< 0.1%
-703870.299 1
< 0.1%
-613560.7959 1
< 0.1%
-497056.3389 1
< 0.1%
-450388.9825 1
< 0.1%
-424448.4375 1
< 0.1%
-409866.166 1
< 0.1%
-408933.5692 1
< 0.1%
-364845.6755 1
< 0.1%
Value Count Frequency (%)
47.23228124 1
< 0.1%
44.19872864 1
< 0.1%
42.4465221 1
< 0.1%
35.50573457 1
< 0.1%
34.45740278 1
< 0.1%
34.08359317 1
< 0.1%
33.28758486 1
< 0.1%
31.85564911 1
< 0.1%
31.5168847 1
< 0.1%
29.72313926 1
< 0.1%

gam_score_90pse
Real number (ℝ)

High correlation  Skewed 

Distinct 3201135
Distinct (%) 99.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 5.8540032
Minimum -5.3409881
Maximum 1572004.1
Zeros 0
Zeros (%) 0.0%
Negative 70408
Negative (%) 2.2%
Memory size 24.6 MiB
2025-04-28T20:34:24.190390 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum -5.3409881
5-th percentile 0.0016412704
Q1 0.0066339165
median 0.44092014
Q3 2.5637388
95-th percentile 7.5232519
Maximum 1572004.1
Range 1572009.4
Interquartile range (IQR) 2.5571049

Descriptive statistics

Standard deviation 1400.7648
Coefficient of variation (CV) 239.28323
Kurtosis 567252.78
Mean 5.8540032
Median Absolute Deviation (MAD) 0.43905083
Skewness 635.22388
Sum 18884189
Variance 1962142.1
Monotonicity Not monotonic
2025-04-28T20:34:24.390034 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
39.46598457 6
 
< 0.1%
118.9437478 6
 
< 0.1%
98.97727784 6
 
< 0.1%
81.45374464 6
 
< 0.1%
65.76001766 6
 
< 0.1%
52.3762249 6
 
< 0.1%
43.106841 6
 
< 0.1%
9.753552263 5
 
< 0.1%
8.723960247 5
 
< 0.1%
8.664628912 5
 
< 0.1%
Other values (3201125) 3225802
> 99.9%
Value Count Frequency (%)
-5.34098812 1
< 0.1%
-4.881815974 1
< 0.1%
-4.879823252 1
< 0.1%
-4.54468279 1
< 0.1%
-4.46198962 1
< 0.1%
-4.425665167 1
< 0.1%
-4.409437007 1
< 0.1%
-4.300086318 1
< 0.1%
-4.155693895 1
< 0.1%
-4.053676269 1
< 0.1%
Value Count Frequency (%)
1572004.067 1
< 0.1%
710328.9383 1
< 0.1%
703627.9047 1
< 0.1%
615014.4754 1
< 0.1%
496292.6023 1
< 0.1%
451424.5824 1
< 0.1%
423917.16 1
< 0.1%
409722.9531 1
< 0.1%
408906.3241 1
< 0.1%
364303.7678 1
< 0.1%

D
Real number (ℝ)

Zeros 

Distinct 633
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 329.8544
Minimum 0
Maximum 7849
Zeros 126892
Zeros (%) 3.9%
Negative 0
Negative (%) 0.0%
Memory size 24.6 MiB
2025-04-28T20:34:24.590409 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 1
Q1 17
median 76
Q3 309
95-th percentile 1469
Maximum 7849
Range 7849
Interquartile range (IQR) 292

Descriptive statistics

Standard deviation 716.44723
Coefficient of variation (CV) 2.1720105
Kurtosis 30.457729
Mean 329.8544
Median Absolute Deviation (MAD) 72
Skewness 4.8510509
Sum 1.0640638 × 109
Variance 513296.63
Monotonicity Not monotonic
2025-04-28T20:34:24.786952 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 126892
 
3.9%
1 88764
 
2.8%
2 74613
 
2.3%
5 64044
 
2.0%
3 54037
 
1.7%
4 49001
 
1.5%
8 41707
 
1.3%
6 41397
 
1.3%
7 39833
 
1.2%
9 35431
 
1.1%
Other values (623) 2610140
80.9%
Value Count Frequency (%)
0 126892
3.9%
1 88764
2.8%
2 74613
2.3%
3 54037
1.7%
4 49001
 
1.5%
5 64044
2.0%
6 41397
 
1.3%
7 39833
 
1.2%
8 41707
 
1.3%
9 35431
 
1.1%
Value Count Frequency (%)
7849 466
 
< 0.1%
6927 3461
0.1%
6538 2301
0.1%
5923 2815
0.1%
5659 3461
0.1%
5506 3840
0.1%
5068 3775
0.1%
5053 2346
0.1%
4975 2449
0.1%
4628 2815
0.1%

E
Real number (ℝ)

Zeros 

Distinct 669
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 96.446233
Minimum 0
Maximum 7397
Zeros 538729
Zeros (%) 16.7%
Negative 0
Negative (%) 0.0%
Memory size 24.6 MiB
2025-04-28T20:34:24.981027 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 2
median 11
Q3 56
95-th percentile 448
Maximum 7397
Range 7397
Interquartile range (IQR) 54

Descriptive statistics

Standard deviation 323.39967
Coefficient of variation (CV) 3.3531602
Kurtosis 123.79768
Mean 96.446233
Median Absolute Deviation (MAD) 11
Skewness 9.0930663
Sum 3.1112195 × 108
Variance 104587.34
Monotonicity Not monotonic
2025-04-28T20:34:25.172235 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 538729
 
16.7%
1 254758
 
7.9%
2 165395
 
5.1%
3 131393
 
4.1%
4 103933
 
3.2%
5 89320
 
2.8%
6 72786
 
2.3%
7 66875
 
2.1%
8 60556
 
1.9%
9 52737
 
1.6%
Other values (659) 1689377
52.4%
Value Count Frequency (%)
0 538729
16.7%
1 254758
7.9%
2 165395
 
5.1%
3 131393
 
4.1%
4 103933
 
3.2%
5 89320
 
2.8%
6 72786
 
2.3%
7 66875
 
2.1%
8 60556
 
1.9%
9 52737
 
1.6%
Value Count Frequency (%)
7397 799
< 0.1%
4972 789
< 0.1%
4286 799
< 0.1%
4284 733
< 0.1%
4138 772
< 0.1%
4087 815
< 0.1%
4024 542
< 0.1%
3345 768
< 0.1%
3339 789
< 0.1%
3015 772
< 0.1%

DE
Real number (ℝ)

Skewed  Zeros 

Distinct 376
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.72117969
Minimum 0
Maximum 3193
Zeros 2453256
Zeros (%) 76.0%
Negative 0
Negative (%) 0.0%
Memory size 24.6 MiB
2025-04-28T20:34:25.361676 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 0
Q3 0
95-th percentile 3
Maximum 3193
Range 3193
Interquartile range (IQR) 0

Descriptive statistics

Standard deviation 5.9236867
Coefficient of variation (CV) 8.2138845
Kurtosis 61924.677
Mean 0.72117969
Median Absolute Deviation (MAD) 0
Skewness 168.53271
Sum 2326424
Variance 35.090064
Monotonicity Not monotonic
2025-04-28T20:34:25.555156 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 2453256
76.0%
1 468144
 
14.5%
2 122064
 
3.8%
3 55385
 
1.7%
4 31452
 
1.0%
5 20519
 
0.6%
6 14116
 
0.4%
7 10203
 
0.3%
8 7690
 
0.2%
9 5964
 
0.2%
Other values (366) 37066
 
1.1%
Value Count Frequency (%)
0 2453256
76.0%
1 468144
 
14.5%
2 122064
 
3.8%
3 55385
 
1.7%
4 31452
 
1.0%
5 20519
 
0.6%
6 14116
 
0.4%
7 10203
 
0.3%
8 7690
 
0.2%
9 5964
 
0.2%
Value Count Frequency (%)
3193 1
< 0.1%
2629 1
< 0.1%
2611 1
< 0.1%
1747 1
< 0.1%
1697 1
< 0.1%
1591 1
< 0.1%
1451 1
< 0.1%
1239 1
< 0.1%
1163 1
< 0.1%
1096 1
< 0.1%

ade_name
Text

Distinct 459152
Distinct (%) 14.2%
Missing 0
Missing (%) 0.0%
Memory size 24.6 MiB
2025-04-28T20:34:26.090800 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Length

Max length 132
Median length 110
Mean length 44.127145
Min length 13

Characters and Unicode

Total characters 142347947
Distinct characters 72
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row valsartan and sacubitril and Fatigue
2nd row valsartan and sacubitril and Fatigue
3rd row valsartan and sacubitril and Fatigue
4th row valsartan and sacubitril and Fatigue
5th row valsartan and sacubitril and Fatigue
Value Count Frequency (%)
and 3269686
 
19.2%
systemic 1071966
 
6.3%
oral 1044638
 
6.1%
parenteral 407190
 
2.4%
rectal 261247
 
1.5%
increased 144872
 
0.8%
topical 141484
 
0.8%
disorder 126266
 
0.7%
decreased 115710
 
0.7%
infection 114625
 
0.7%
Other values (6618) 10358740
60.7%
2025-04-28T20:34:26.658492 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

Value Count Frequency (%)
a 14414575
 
10.1%
13830565
 
9.7%
e 12028982
 
8.5%
i 10656590
 
7.5%
n 10631306
 
7.5%
r 8805272
 
6.2%
o 8756832
 
6.2%
t 7984823
 
5.6%
s 7215432
 
5.1%
l 7079660
 
5.0%
Other values (62) 40943910
28.8%

Most occurring categories

Value Count Frequency (%)
(unknown) 142347947
100.0%

Most frequent character per category

(unknown)
Value Count Frequency (%)
a 14414575
 
10.1%
13830565
 
9.7%
e 12028982
 
8.5%
i 10656590
 
7.5%
n 10631306
 
7.5%
r 8805272
 
6.2%
o 8756832
 
6.2%
t 7984823
 
5.6%
s 7215432
 
5.1%
l 7079660
 
5.0%
Other values (62) 40943910
28.8%

Most occurring scripts

Value Count Frequency (%)
(unknown) 142347947
100.0%

Most frequent character per script

(unknown)
Value Count Frequency (%)
a 14414575
 
10.1%
13830565
 
9.7%
e 12028982
 
8.5%
i 10656590
 
7.5%
n 10631306
 
7.5%
r 8805272
 
6.2%
o 8756832
 
6.2%
t 7984823
 
5.6%
s 7215432
 
5.1%
l 7079660
 
5.0%
Other values (62) 40943910
28.8%

Most occurring blocks

Value Count Frequency (%)
(unknown) 142347947
100.0%

Most frequent character per block

(unknown)
Value Count Frequency (%)
a 14414575
 
10.1%
13830565
 
9.7%
e 12028982
 
8.5%
i 10656590
 
7.5%
n 10631306
 
7.5%
r 8805272
 
6.2%
o 8756832
 
6.2%
t 7984823
 
5.6%
s 7215432
 
5.1%
l 7079660
 
5.0%
Other values (62) 40943910
28.8%

Interactions

2025-04-28T20:34:01.097314 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:10.193986 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:16.584387 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:21.922237 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:27.378029 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:33.053702 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:38.714370 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:44.154236 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:49.654864 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:55.286618 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:34:01.660911 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:11.594312 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:17.076694 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:22.428473 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:27.897229 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:33.594204 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:39.222476 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:44.665336 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:50.306881 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:55.865845 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:34:02.169049 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:12.120551 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:17.579910 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:22.898066 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:28.496030 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:34.164457 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:39.795843 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:45.238467 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:50.830589 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:56.661799 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:34:02.665213 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:12.628863 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:18.082333 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:23.505782 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:29.061239 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:34.720364 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:40.351886 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:45.784835 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:51.342549 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:57.161754 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:34:03.165435 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:13.166141 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:18.568421 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:24.067587 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:29.649174 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:35.198418 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:40.929980 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:46.351770 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:51.855837 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:57.670530 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:34:03.666019 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:13.660123 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:19.067856 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:24.676074 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:30.244474 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:35.774993 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:41.421577 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:46.936207 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:52.372462 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:58.225945 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:34:04.175712 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:14.203322 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:19.587671 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:25.245741 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:30.832410 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:36.594100 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:42.043127 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:47.440362 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:52.946937 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:58.754851 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:34:04.775131 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:14.796542 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:20.195299 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:25.798764 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:31.430871 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:37.167888 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:42.594175 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:48.005078 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:53.493379 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:59.380765 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:34:05.381250 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:15.402977 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:20.798362 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:26.333973 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:31.971586 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:37.679923 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:43.120551 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:48.526615 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:54.120324 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:59.900409 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:34:05.885074 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:15.975497 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:21.394280 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:26.839478 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:32.548289 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:38.196548 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:43.642985 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:49.046870 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:33:54.702451 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
2025-04-28T20:34:00.500751 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-04-28T20:34:26.812939 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
atc_concept_id meddra_concept_id gam_score norm gam_score_se gam_score_90mse gam_score_90pse D E DE
atc_concept_id 1.000 -0.000 0.017 -0.000 -0.000 0.000 -0.000 -0.051 0.040 -0.007
meddra_concept_id -0.000 1.000 0.007 0.001 -0.001 0.001 -0.001 0.007 0.007 0.001
gam_score 0.017 0.007 1.000 0.218 -0.085 0.087 -0.083 -0.018 -0.053 0.010
norm -0.000 0.001 0.218 1.000 -0.001 0.001 -0.000 0.087 0.021 0.053
gam_score_se -0.000 -0.001 -0.085 -0.001 1.000 -1.000 1.000 -0.001 -0.000 -0.000
gam_score_90mse 0.000 0.001 0.087 0.001 -1.000 1.000 -1.000 0.001 0.000 0.000
gam_score_90pse -0.000 -0.001 -0.083 -0.000 1.000 -1.000 1.000 -0.001 -0.000 -0.000
D -0.051 0.007 -0.018 0.087 -0.001 0.001 -0.001 1.000 -0.004 0.160
E 0.040 0.007 -0.053 0.021 -0.000 0.000 -0.000 -0.004 1.000 0.203
DE -0.007 0.001 0.010 0.053 -0.000 0.000 -0.000 0.160 0.203 1.000
2025-04-28T20:34:27.012996 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
atc_concept_id meddra_concept_id gam_score norm gam_score_se gam_score_90mse gam_score_90pse D E DE
atc_concept_id 1.000 0.019 -0.018 -0.002 -0.029 0.010 -0.029 0.016 -0.001 0.002
meddra_concept_id 0.019 1.000 0.015 0.001 0.016 -0.005 0.013 0.009 -0.028 -0.008
gam_score -0.018 0.015 1.000 0.380 0.548 0.309 0.713 -0.089 -0.153 0.089
norm -0.002 0.001 0.380 1.000 0.033 0.383 0.146 0.162 0.152 0.256
gam_score_se -0.029 0.016 0.548 0.033 1.000 -0.346 0.927 -0.199 -0.139 -0.039
gam_score_90mse 0.010 -0.005 0.309 0.383 -0.346 1.000 -0.125 0.155 0.001 0.192
gam_score_90pse -0.029 0.013 0.713 0.146 0.927 -0.125 1.000 -0.208 -0.147 0.003
D 0.016 0.009 -0.089 0.162 -0.199 0.155 -0.208 1.000 0.031 0.344
E -0.001 -0.028 -0.153 0.152 -0.139 0.001 -0.147 0.031 1.000 0.390
DE 0.002 -0.008 0.089 0.256 -0.039 0.192 0.003 0.344 0.390 1.000
2025-04-28T20:34:27.213835 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
atc_concept_id meddra_concept_id gam_score norm gam_score_se gam_score_90mse gam_score_90pse D E DE
atc_concept_id 1.000 0.013 -0.012 -0.001 -0.019 0.007 -0.019 0.011 -0.001 0.002
meddra_concept_id 0.013 1.000 0.010 0.001 0.011 -0.004 0.008 0.006 -0.019 -0.006
gam_score -0.012 0.010 1.000 0.266 0.418 0.191 0.571 -0.060 -0.104 0.070
norm -0.001 0.001 0.266 1.000 0.024 0.270 0.101 0.110 0.105 0.206
gam_score_se -0.019 0.011 0.418 0.024 1.000 -0.391 0.847 -0.134 -0.086 -0.031
gam_score_90mse 0.007 -0.004 0.191 0.270 -0.391 1.000 -0.238 0.104 -0.010 0.151
gam_score_90pse -0.019 0.008 0.571 0.101 0.847 -0.238 1.000 -0.141 -0.091 0.002
D 0.011 0.006 -0.060 0.110 -0.134 0.104 -0.141 1.000 0.021 0.274
E -0.001 -0.019 -0.104 0.105 -0.086 -0.010 -0.091 0.021 1.000 0.319
DE 0.002 -0.006 0.070 0.206 -0.031 0.151 0.002 0.274 0.319 1.000
2025-04-28T20:34:27.416323 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
atc_concept_id meddra_concept_id nichd gam_score norm gam_score_se gam_score_90mse gam_score_90pse D E DE
atc_concept_id 1.000 0.017 0.000 0.000 0.013 0.000 0.000 0.000 0.047 0.041 0.000
meddra_concept_id 0.017 1.000 0.000 0.015 0.014 0.000 0.000 0.000 0.020 0.077 0.004
nichd 0.000 0.000 1.000 0.013 0.860 0.001 0.001 0.001 0.345 0.153 0.006
gam_score 0.000 0.015 0.013 1.000 0.026 0.714 0.714 0.714 0.008 0.000 0.000
norm 0.013 0.014 0.860 0.026 1.000 0.003 0.003 0.003 0.233 0.064 0.003
gam_score_se 0.000 0.000 0.001 0.714 0.003 1.000 1.000 1.000 0.000 0.000 0.000
gam_score_90mse 0.000 0.000 0.001 0.714 0.003 1.000 1.000 1.000 0.000 0.000 0.000
gam_score_90pse 0.000 0.000 0.001 0.714 0.003 1.000 1.000 1.000 0.000 0.000 0.000
D 0.047 0.020 0.345 0.008 0.233 0.000 0.000 0.000 1.000 0.010 0.086
E 0.041 0.077 0.153 0.000 0.064 0.000 0.000 0.000 0.010 1.000 0.060
DE 0.000 0.004 0.006 0.000 0.003 0.000 0.000 0.000 0.086 0.060 1.000

Missing values

2025-04-28T20:34:06.602176 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-28T20:34:09.576210 image/svg+xml Matplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

atc_concept_id meddra_concept_id ade nichd gam_score norm gam_score_se gam_score_90mse gam_score_90pse D E DE ade_name
0 1588648 35809076 1588648_35809076 term_neonatal -0.130600 0.000000 2.478017 -4.206938 3.945738 0 20 0 valsartan and sacubitril and Fatigue
1 1588648 35809076 1588648_35809076 infancy 0.947295 0.165652 1.982215 -2.313449 4.208039 0 80 0 valsartan and sacubitril and Fatigue
2 1588648 35809076 1588648_35809076 toddler 2.028237 0.331773 1.794061 -0.922994 4.979467 0 107 0 valsartan and sacubitril and Fatigue
3 1588648 35809076 1588648_35809076 early_childhood 3.114764 0.498751 1.859835 0.055336 6.174192 0 294 0 valsartan and sacubitril and Fatigue
4 1588648 35809076 1588648_35809076 middle_childhood 4.206369 0.666511 2.103987 0.745311 7.667428 0 1046 0 valsartan and sacubitril and Fatigue
5 1588648 35809076 1588648_35809076 early_adolescence 5.296451 0.834036 2.519912 1.151196 9.441707 1 2697 1 valsartan and sacubitril and Fatigue
6 1588648 35809076 1588648_35809076 late_adolescence 6.376378 1.000000 3.140563 1.210152 11.542603 0 1729 0 valsartan and sacubitril and Fatigue
7 1588648 36315755 1588648_36315755 term_neonatal -0.309645 0.000000 5.605292 -9.530350 8.911061 0 0 0 valsartan and sacubitril and Urine output increased
8 1588648 36315755 1588648_36315755 infancy 2.245978 0.165652 4.433657 -5.047387 9.539343 0 2 0 valsartan and sacubitril and Urine output increased
9 1588648 36315755 1588648_36315755 toddler 4.808824 0.331773 3.794585 -1.433269 11.050917 0 6 0 valsartan and sacubitril and Urine output increased
atc_concept_id meddra_concept_id ade nichd gam_score norm gam_score_se gam_score_90mse gam_score_90pse D E DE ade_name
3225849 45893497 37522270 45893497_37522270 middle_childhood 2.472841 0.635989 0.927559 0.947007 3.998675 5 51 0 cabozantinib and Surgery
3225850 45893497 37522270 45893497_37522270 early_adolescence 3.243015 0.813670 0.999720 1.598476 4.887555 12 131 0 cabozantinib and Surgery
3225851 45893497 37522270 45893497_37522270 late_adolescence 4.050683 1.000000 1.206338 2.066257 6.035108 6 94 1 cabozantinib and Surgery
3225852 45893497 37622518 45893497_37622518 term_neonatal -0.028672 0.000000 0.901623 -1.511841 1.454497 0 13 0 cabozantinib and Haemorrhage
3225853 45893497 37622518 45893497_37622518 infancy 0.348263 0.168004 0.735998 -0.862454 1.558979 0 41 0 cabozantinib and Haemorrhage
3225854 45893497 37622518 45893497_37622518 toddler 0.725684 0.336224 0.707710 -0.438499 1.889866 0 28 0 cabozantinib and Haemorrhage
3225855 45893497 37622518 45893497_37622518 early_childhood 1.103589 0.504661 0.784264 -0.186525 2.393703 0 68 0 cabozantinib and Haemorrhage
3225856 45893497 37622518 45893497_37622518 middle_childhood 1.480633 0.672713 0.921456 -0.035162 2.996428 5 129 0 cabozantinib and Haemorrhage
3225857 45893497 37622518 45893497_37622518 early_adolescence 1.852677 0.838537 1.105131 0.034737 3.670617 12 450 1 cabozantinib and Haemorrhage
3225858 45893497 37622518 45893497_37622518 late_adolescence 2.214936 1.000000 1.341605 0.007995 4.421876 6 448 0 cabozantinib and Haemorrhage